The Dawn of AI: Embracing the Data Science

June 26, 2017 | Innovations | Infopulse

Artificial Intelligence in a broad sense is all about a long-lasting attempt to simulate human cognitive processes and behavior by computer systems. Cognition and related behavior include such processes as learning, reasoning, and based on them constant self-correction. These processes as a whole are often called “training”, so AI is designed and trained either for a specific task, or for a variety of tasks. Depending on a particular application, the AI system can also include speech recognition and machine vision in its arsenal. Despite the common fears, AI aims to improve services and products, not to replace humans at work.

A lot has been said about the Artificial Intelligence. TCS, Gartner, IDS and many other reports state that the current rise of AI resembles the dawn of the Internet at the beginning of the 90s. The Internet as we know it has changed our world forever. In just 10 years, our perception of life will change dramatically again. Innovative technologies based on Big Data processing and cognitive cloud computing are advancing at an exponential rate. Led by the innovation evangelists, tech leaders, and IT experts, the revolution is already here. Among others, these are data scientists who are shaping our reality.

Previously, we spoke about Data Science in plain terms. In our new blog post, read why IT engineers moved from regular programming, why data science is important for our world and how Infopulse conducts data science developments.

AI Is Coming

Many aspects of our life can be represented in a form of data and statistics. Extraction of insights and knowledge from data is what data science is about. Called the sexiest job of the 21st century by Harward Business Review, Data Science has paved a way to innovations like never seen before. AI, machine-to-machine learning, neural networks and complex solutions stand behind the latest smart city projects, self-driving cars and your personal mobile assistants like Siri. However, only recently Data Science has started to gain in popularity.

We will definitely see a massive shift in the labor force. According to a report by PwC or another report by McKinsey Global Institute, big data processing and robotics will allow AI to automate 40-50% of existing jobs in just 10-15 years. Ultimately, AI won’t lead to the global unemployment. On the contrary, AI will create a multitude of new jobs and that is already happening today.

A Step Towards Future

While Artificial Intelligence may have some issues yet to be solved, tech giants strive to ensure that AI is not a threat to the humanity. Earlier in 2017, Stephen Hawking, Elon Musk, Bill Gates and thousands of researchers came up with a set of AI Principles to regulate AI development, so that it’s used only to the benefit of the mankind.

Take, for example, the latest gadgets with smart assistants. Mobile phones have become an ultimate part of our interconnected world, understanding what they are shown, knowing personal preferences and having the ability to learn and improve over time. Machines already help to detect illnesses and suggest appropriate treatment. In a long term, AI will both improve standards of our lives and increase longevity through automation, while robots replace humans in jobs with the poor working condition.

New technologies raise need in new jobs and experts. Chatbots are already taking jobs in commercial, consulting and legal companies. During the ChatBot Conference UA 2017, speakers reviewed a case of chatbot service used in PrivatBank, one of the largest and technologically innovative banks of Ukraine. At least 65% of bank clients’ requests are currently processed with chatbots, resulting in UAH 48Mln+ (USD 1.84Mln) of savings yearly. This is just one example, while as of lately, the whole world has seen a massive expansion of data science projects, resulting in a growing need for data scientists.

Why Data Science Is Popular More Than Ever

There are at least several reasons, why Data Science area is growing fast:

Accessibility of high-productivity computing.According to the Economist, while conventional processors are still used for servers and regular computing, NVidia’s incredibly powerful and cheap GPUs now serve AI, cloud computing, and Big Data. The price for a powerful GPU, suitable for data science computing, has dropped significantly from thousands of bucks to just some hundreds. You can easily buy a hi-end rig or rent cloud processing capacities from Amazon and enter the world of Data Science.

Software market exploded with new products with unprecedented added value. Powered by Global companies like Google, Facebook or Microsoft, products or rather platforms empower other companies or enthusiasts to offer new services, develop innovative solutions, etc. E.g., Alphabet has recently transferred their Google translator to machine-to-machine algorithms. This vastly increased the accuracy of the translation and allowed other companies to implement it for day-to-day translations of websites or services.

Accessibility of knowledge. With lots of courses, meetups, and online learning platforms available, new skills and experience are just a click away.

These are just some of the main factors, why learning data science has become a new aim for both beginners and experienced programmers. You hear it right: data science takes skill. Having good programming skills, being a good software engineer and knowing architecture is not enough.

What It Takes to Be a Data Scientist

One of the best ways to become better at data science is to participate in the world’s largest events, like Kaggle Data Science Bowl. Main prizes for winning a competition may reach hundreds of thousands of USD. Even if you don’t win the first place, your concept or model may still be considered for production. Normally, customers review all projects before selecting an optimal model. Here’s an example: in Kaggle Data Science Bowl prizes are awarded to the first 10 teams, plus a total number of participants multiplied by a factor of 0.002. Thus, even participation in such an event and designing a working Data Science project is already a victory, as it can give a huge experience boost.

Naturally, it’s dangerous to go alone. Teamwork is the key to the victory.

How Infopulse Participates in Data Science Projects in Ukraine and Abroad

The Infopulse team regularly takes part in Data Science events and it’s totally worth it! This helps us to broaden our knowledge, trial-test our skills, and brainstorm new ideas. E.g., our recent MyParking project was developed during an IoT Hackathon and later enhanced using computer vision. Our participation in the latest Kaggle Data Science Bowl 2017 with a cancer detection project is a pure AI development based on neural networks and machine learning. All new knowledge, skills, and expertise we apply to build products for our customers. Among our recent Data Science products are:

A solution for One of the Big 4, based on SAP HANA and R-language to label business data.

Another solution for one of our customers in Healthcare domain is a mobile application, which detects stroke signs by analyzing face video and speech with computer vision and automatically informs hospital about the patient’s name, location, mobile phone, and insurance ID.

A solution for Infopulse HR department based on Big Data and SAP HANA with LinkedIn-like functionality, which helps to analyze candidates’ CVs and quickly processes large volumes of data per request.

Our latest data science project for EVRY is a chatbot for social networks based on Natural Language Processing approach, featuring a variety of personalities and supporting a wide range of languages.

Naturally, we don’t stop here, as we continue exploring the endless possibilities of Data Science, Big Data, Machine Learning and AI in general.

In our next article, we’ll be giving an in-depth coverage of our participation in Kaggle Data Science Bowl 2017 with a cancer detection project.

Data Science helps us building great products. Want to talk about it? Contact us today!